Supporting Relative Debugging for Large-scale UPC Programs

Dinh, Minh Ngoc; Abramson, David; Jin, Chao; DeRose, Luiz; Moench, Bob; Gontarek, Andrew

doi:10.1016/j.procs.2014.05.135

Cited by 2 publications

(1 citation statement)

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“…Our previous work, Guard [26] [35], investigated validating the global state of MPI programs [35] and UPC programs [36] respectively, but could not handle any hybrid applications and data comparison across different types of applications. In this paper relative debugging has been extended significantly to facilitate data comparison across different programming techniques, such as UPC, hybrid MPI/OpenACC, and MPI/OpenMP programs, on hybrid machines.…”

Section: Related Workmentioning

confidence: 99%

Relative debugging for a highly parallel hybrid computer system

DeRose¹,

Gontarek²,

Vose³

et al. 2015

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Relative debugging traces software errors by comparing two executions of a program concurrently-one code being a reference version and the other faulty. Relative debugging is particularly effective when code is migrated from one platform to another, and this is of significant interest for hybrid computer architectures containing CPUs accelerators or coprocessors. In this paper we extend relative debugging to support porting stencil computation on a hybrid computer. We describe a generic data model that allows programmers to examine the global state across different types of applications, including MPI/OpenMP, MPI/OpenACC, and UPC programs. We present case studies using a hybrid version of the 'stellarator' particle simulation DELTA5D, on Titan at ORNL, and the UPC version of Shallow Water Equations on Crystal, an internal supercomputer of Cray. These case studies used up to 5,120 GPUs and 32,768 CPU cores to illustrate that the debugger is effective and practical.

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Section: Related Workmentioning

confidence: 99%